# convolutionFFT2D - FFT-Based 2D Convolution

## Description

This sample demonstrates how 2D convolutions with very large kernel sizes can be efficiently implemented using FFT transformations.

## Key Concepts

Image Processing, CUFFT Library

## Supported SM Architectures

[SM 5.0 ](https://developer.nvidia.com/cuda-gpus)  [SM 5.2 ](https://developer.nvidia.com/cuda-gpus)  [SM 5.3 ](https://developer.nvidia.com/cuda-gpus)  [SM 6.0 ](https://developer.nvidia.com/cuda-gpus)  [SM 6.1 ](https://developer.nvidia.com/cuda-gpus)  [SM 7.0 ](https://developer.nvidia.com/cuda-gpus)  [SM 7.2 ](https://developer.nvidia.com/cuda-gpus)  [SM 7.5 ](https://developer.nvidia.com/cuda-gpus)  [SM 8.0 ](https://developer.nvidia.com/cuda-gpus)  [SM 8.6 ](https://developer.nvidia.com/cuda-gpus)  [SM 8.7 ](https://developer.nvidia.com/cuda-gpus)  [SM 8.9 ](https://developer.nvidia.com/cuda-gpus)  [SM 9.0 ](https://developer.nvidia.com/cuda-gpus)

## Supported OSes

Linux, Windows

## Supported CPU Architecture

x86_64, armv7l

## CUDA APIs involved

### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
cudaMemcpy, cudaFree, cudaDestroyTextureObject, cudaDeviceSynchronize, cudaCreateTextureObject, cudaMemset, cudaMalloc

## Dependencies needed to build/run
[CUFFT](../../../README.md#cufft)

## Prerequisites

Download and install the [CUDA Toolkit 12.5](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
Make sure the dependencies mentioned in [Dependencies]() section above are installed.

## References (for more details)